MCP Base

Overview

The MCP Server Boilerplate provides a lightweight, production‑ready foundation for building Model Context Protocol (MCP) servers in Node.js. By exposing custom tools, prompts, and resources over a standardized protocol, the server enables large‑language‑model (LLM) powered IDEs—such as Cursor AI—to extend their capabilities with domain‑specific logic. Instead of hard‑coding tool calls into the LLM, developers can package reusable functionality in a separate process that the AI client invokes on demand. This separation of concerns improves maintainability, security, and scalability.

At its core, the boilerplate ships with two example tools: an addition function that accepts two numeric arguments and returns their sum, and a getApiKey helper that retrieves an API key from the environment. These simple examples illustrate how to define input schemas, validate parameters with Zod, and produce structured outputs that the LLM can parse. The server also includes a predefined add_numbers prompt, demonstrating how to embed tool usage instructions directly into the model context so that the assistant can automatically infer when to call the addition tool.

Key features of this MCP server include:

• Standard I/O Transport: The server communicates via , making it compatible with any LLM client that can spawn a child process, such as Cursor AI or the MCP Inspector.
• Schema Validation: Input parameters are rigorously checked using Zod, ensuring that only well‑formed data reaches the tool logic and preventing runtime errors.
• Prompt Injection: By exposing a prompt template, developers can guide the model to use specific tools without hard‑coding prompts into the client.
• Environment Variable Handling: The tool demonstrates how to safely read configuration from the environment, a pattern that can be extended to any sensitive data source.

Real‑world use cases abound. A data‑science team can expose a tool that queries a proprietary database and returns aggregated metrics, while a web‑dev squad could provide a function that generates deployment scripts. In each scenario, the MCP server decouples business logic from the AI model, allowing developers to update tools independently of the LLM. Integration is straightforward: the client launches the MCP server as a child process, sends tool invocation requests over stdin/stdout, and receives structured JSON responses that the assistant can immediately incorporate into its output.

What sets this boilerplate apart is its minimalistic yet extensible design. It ships with a fully functional development workflow—complete with an MCP Inspector for interactive testing—and follows best practices such as ES‑module support, script automation, and clear separation of configuration. Developers familiar with MCP can therefore focus on crafting domain‑specific tools while relying on a proven, battle‑tested foundation that guarantees seamless interoperability with any LLM‑based IDE.